python+opencv Canny边缘检测_50行canny边缘检测算法python

Step1：高斯滤波（低通滤波，用高斯滤波器平滑图像）

• 卷积/卷积核（对应相乘再相加）
• 原始图片外围加一圈0，为保证新图片与原始图片大小相同
• 卷积核为3*3，外围补一圈0；卷积核为5*5，外围补两圈0

 Step2：计算图像梯度及梯度方向（图像增强）

 Step3：非极大值抑制（阈值设置）

 Step4：双阈值筛选边缘

#---------------------------------边缘检测---------------------------------
import cv2
 
class MainWindow():
 
    def __init__(self):
        super().__init__()
        img = cv2.imread("D:/Users/xuyf2/Desktop/cp1635564192.jpg")
        print('img_shape', img.shape)
        src0 = cv2.resize(img, (640, 480))
 
        # src1 = src0[0:480, 365:515]                    # 裁切需要的部分
        scr1 = cv2.medianBlur(src0, 5)                   # 中值滤波
        self.line_detect_possible_demo(scr1)
 
    def line_detect_possible_demo(self,image):           # 检测出可能的线段
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray, 200,610, apertureSize=3)
        print('edges_shape', edges.shape)
        cv2.imshow("Canny", edges)
 
if __name__ == '__main__':
 
    mainWindow = MainWindow()
    cv2.waitKey(0)
    cv2.destroyAllWindows()
 

• 寻找角点 

import cv2
import numpy as np
 
cover_path = "D:/Users/xuyf2/Desktop/lego_jier/cp1622130065.jpg"
im1 = cv2.imread(cover_path)           # 读取图片
im2 = cv2.resize(im1,(640,480),)       # 缩放图片
img = im2[0:400, 80:300]               # 裁剪图片
scr1 = cv2.medianBlur(img, 5)          # 中值滤波
 
gray = cv2.cvtColor(scr1, cv2.COLOR_BGR2GRAY)      # 灰度处理
edges = cv2.Canny(gray, 150,400, apertureSize=3)   # 灰度处理
cv2.imshow("Canny", edges)                         # 边缘检测
gra = np.float32(edges)
# 角点坐标
corners = cv2.goodFeaturesToTrack(gra, 50, 0.1, 50)
corners = np.int0(corners)
 
for i in corners:                                  # 降维
    x, y = i.ravel()
    print([x,y])                                   # 打印角点坐标
    cv2.circle(img, (x, y), 6, (0, 0, 255), -1)
 
cv2.imwrite('jiaodian.jpg', img)
cv2.imshow('jiaodian', img)
cv2.waitKey(0)